A deep learning model trained with real-time satellite data significantly reduces the time to predict the ground motion of big earthquakes.
Editors’ Highlights
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Pre-Season Wet Soil Produces Fire-Prone Conditions
The SMAP satellite shows that wetter-than-normal soil five months prior to wildfires in the western United States increases fuel availability and fire activity when desiccation occurs.
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Rainstorm Intensity Drives Desert Landscape Evolution
New mathematical models show that the persistence of near-vertical cliffs in arid landscapes is maintained by infrequent, but high-intensity rain storms.
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Machine Learning for Geochemists Who Don’t Want to Code
Geochemistry π is an easy-to-use step-by-step interface to carry out common machine learning tasks on geochemical data, including regression, clustering, classification, and dimension-reduction.
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Solar Eclipses May Initiate Disturbances in Geospace
The statistical evidence from 21 years of data suggests that a solar eclipse may trigger a geomagnetic substorm, which is a disturbance in the Earth’s magnetosphere and ionosphere.
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Using Machine Learning to Reconstruct Cloud-Obscured Dust Plumes
Satellite-observed dust plumes from North Africa are frequently obscured by clouds, but a new study uses machine learning to reconstruct dust patterns, demonstrating a new way to validate dust forecasts.
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A Dust-Up Over Dust Measurements
Dust has significant impacts on the environment, climate, air quality, and human health, yet dust events are underestimated and therefore do not receive the level of attention necessary.
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Deep Learning Tackles Deep Uncertainty
A new method based on artificial intelligence could help accelerate projections of polar ice melt and future sea level rise.
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New Tracers of Wind Erosion Provide Insight into Dryland Vegetation
Rare earth element tracers provide insight into how fire and wind transport influence the vegetation state of the world’s drylands.
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Measuring Link Between the Chemistry and Physics of the Atmosphere
A new study sheds light on the coupling between the chemical composition and the physical properties of the atmosphere.